As society continues to develop data manipulation techniques, immediate verification of data has become possible. In some instances, verification includes cloud-based verification of information. This cloud-based data verification occurs via the “cleansing” of supplied data. Cleansing data may include validating received data and/or supplying missing information to provide a more complete representation of the data. For example, websites sometimes cleanse address data provided by users to obtain more accurate address representations.
At times, however, users and/or systems may submit mistaken cleanse requests, requesting a cleansed version of blank data, or mostly blank data, or unintentionally requesting cleansing services for incomplete data. Users and/or systems may also submit requests that are deficient in a manner where cleansing would provide no meaningful benefit. These deficient cleanse requests result in the wasteful usage of system resources to cleanse incomplete or deficient data. Further, when utilizing cleansing services requires monetary payment, funds are wasted on unintended cleanses. Preventing unintended cleanses may aid in preventing the wasteful allocation system resources as well as preventing wasteful spending on cleansing costs.
In the drawings, like reference numbers generally indicate identical or similar elements. Additionally, generally, the left-most digit(s) of a reference number identifies the drawing in which the reference number first appears